app.py

import huggingface_hub as hf_hub
import time
import openvino_genai as ov_genai
import numpy as np
import gradio as gr
import re
import gc


hf_hub.snapshot_download(repo_id="OpenVINO/Qwen3-0.6B-int4-ov", local_dir="ov", local_dir_use_symlinks=False)

# 初始化模型
device = "CPU"
InUsed_model_name = "ov"
model_path = f"./{InUsed_model_name}"  # 加上目錄路徑
pipe = ov_genai.LLMPipeline(model_path, device)
tokenizer = pipe.get_tokenizer()
config = ov_genai.GenerationConfig(max_new_tokens=4096)

# 推理函式
def generate_response(prompt, model_name):
    global pipe, tokenizer
    pipe = ov_genai.LLMPipeline("ov", device)
    tokenizer = pipe.get_tokenizer()
    generated = pipe.generate([prompt], config, streamer)
    tokenpersec = f'{generated.perf_metrics.get_throughput().mean:.2f}'
    print(f"\nModel:{model_name} TPS:{tokenpersec}\n")
    
    return tokenpersec, generated.text

# 建立 Gradio 介面


demo = gr.Interface(
    fn=generate_response,
    inputs=[
        gr.Textbox(lines=5, label="輸入提示 (Prompt)")
    ],
    outputs=[
        gr.Textbox(label="tokens/sec"),
        gr.Textbox(label="回應"),
    ],
    title="Qwen3 Model Inference",
    description="基於 Qwen3 推理應用，支援思考過程分離與 GUI。"
)

if __name__ == "__main__":
    demo.launch()